Many clinical applications require identifying a liver tissue boundary from conventional medical imaging including ultrasonic imaging, magnetic resonance imaging (MRI), computed tomography (CT) and so on, so as to locate a liver examination region, such as for liver elasticity examination and liver color Doppler ultrasonography etc.
At present, the liver tissue boundary is mostly identified manually. However, selecting the liver boundary manually according to liver tissue information requires the operator to be highly familiar with the liver tissue structure and image information so that the liver tissue boundary can be selected accurately, which is very demanding for the operator. Meanwhile, the relatively long time it takes to manually conduct the identification in the identification process can lead to low efficiency in identifying the liver boundary.